Yefim Bakman [1]} touched upon the understanding involving word problems with extraneous information. Mukherjee and Garain [2]} surveyed on different techniques used for word problem solving. Word problems can be represented as a relation model as done by Bobrow [3]}. Multiple Approaches were tried for reasoning across sentences of the problem text. Approaches can be template alignment, prediction of verb categories and solving the problem, using CFG rules along with the generation of equation trees. The system designed by Kushman et al [4]} was a joint log linear distribution over the full set of equations and alignments between the variables and text. The number of equations was determined by the number of training equation templates. The number slots were filled by the numbers present in the text while the unknown or variable slot were filled by the nouns in the problem text. The derivations possible out of the all templates, the derivation with the highest probability score was chosen. Illinois Math Solver [5]} had two modules to solve any arithmetic word problem. First module was a CFG based Semantic Parser that would solve simple arithmetic problems where specific keywords like add, subtract, difference, product, sum, multiply or divide were present. If the problem could not be solved by the first module, the problem was solved by decomposition of the arithmetic problem into a series of classification problems [6]}. The classification outputs were then combined to form an expression tree through constrained inference. Hosseini's [7]} ARIS system used verb categorization. ARIS analyzed the sentences in the problem for identifying relevant variables, their values and mapping this information into a set of linear equations which can be easily solved. The system identified 7 kinds of verbs used in the problems which was predicted by support vector machines. Mitra and Baral [8]} created an arithmetic word problem solver which learned how to use formulas to solve simple addition and subtraction problems. The formulas were modeled as templates with pre-defined slots. They used a log-linear model to find out the best possible formula to solve a problem. The features to the model were dependency labels by running Stanford dependency parser, POS tags, some linguistic cues, Wordnet [9]} features.
